A Nomogram to Predict Benign/Malignant Mediastinal Lymph Nodes Based on EBUS Sonographic Features

Background Endobronchial ultrasound (EBUS) sonographic features help identify benign/malignant lymph nodes while conducting transbronchial needle aspiration (TBNA). This study aims to identify risk factors for malignancy based on EBUS sonographic features and to estimate the risk of malignancy in lymph nodes by constructing a nomogram. Methods 1082 lymph nodes from 625 patients were randomly enrolled in training (n = 760) and validation (n = 322) sets. The subgroup of EBUS-TBNA postoperative negative lymph nodes (n = 317) was randomly enrolled in a training (n = 224) set and a validation (n = 93) set. Logistic regression analysis was used to identify the EBUS features of malignant lymph nodes. A nomogram was formulated using the EBUS features in the training set and later validated in the validation set. Results Multivariate analysis revealed that long-axis, short-axis, echogenicity, fusion, and central hilar structure (CHS) were the independent predictors of malignant lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 5 EBUS features nomogram showed good discrimination, with area under the curve values of 0.880 (sensitivity = 0.829 and specificity = 0.807) and 0.905 (sensitivity = 0.819 and specificity = 0.857). Subgroup multivariate analysis revealed that long-axis, echogenicity, and CHS were the independent predictors of malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes. Based on these risk factors, a nomogram was constructed. Both the training and validation sets of 3 EBUS features nomogram showed good discrimination, with the area under the curve values of 0.890 (sensitivity = 0.882 and specificity = 0.786) and 0.834 (sensitivity = 0.930 and specificity = 0.636). Conclusions Our novel scoring system based on two nomograms can be utilized to predict malignant lymph nodes.


Background
Many diseases involve the mediastinal lymph nodes, and the main causes are tuberculosis, nodal disease, infammation, teratoma, thymoma, lung cancer, metastatic tumors, and lymphoma [1].Identifying benign and malignant mediastinal lymph nodes is crucial to formulating treatment plans and determining the patient's prognosis [2,3].
Many worldwide guidelines recommend EBUS-TBNA for staging lung cancer and diagnosing hilar and mediastinal lesions [4,5].EBUS-TBNA is also reported to have a higher diagnostic accuracy than computed positron emission tomography (PET) and computed tomography (CT) [6].Patients undergoing EBUS-TBNA can be diagnosed with malignancy or benignity based on the sonographic features [7].Hence, sonographic features of hilar and mediastinal lymph nodes have attracted increasing interest.In a retrospective assessment of 1061 lymph nodes, EBUS sonographic features such as shape, short-axis, echogenicity, margin, coagulation necrosis sign (CNS), and absence of CHS were widely used to identify benign or malignant mediastinal lymph nodes [8].EBUS sonographic features are a useful tool to distinguish malignant or benign lymph nodes and can also be used to identify benign intrathoracic lymphadenopathy [9].Tis study predicted tuberculous nodes from two sonographic features (the absence of clustered formation and the presence of necrosis signs) and two sonographic features of each category (absence of clustered formation, hilar perfusion or avascular, and CHS) predicted reactive lymphadenitis, as well as sarcoid nodes was predicted the optimal diagnostic efciency by at least four sonographic features from fve features (short-axis >1 cm, absence of CHS, nonhilar perfusion, margin, and clustered formation) [9].In addition, EBUS sonographic features not only predict benign and malignant lymph nodes but also allow for further identifcation of EBUS-TBNA postoperative negative lymph nodes.A retrospective risk model study of lung cancer patients with negative EBUS-TBNA lymph nodes showed that heterogeneity was an important EBUS sonographic feature for predicting malignant lymph nodes [10].However, to the best of our knowledge, no nomograms are currently available to predict the malignant lymph nodes and the risk stratifcation of lymph nodes deemed negative following EBUS-TBNA based on EBUS sonographic features.
Te purpose of this study was to develop and validate a nomogram that accurately predicts the malignant lymph nodes and the risk stratifcation of lymph nodes deemed negative following EBUS-TBNA based on EBUS sonographic features.Study ethics approval was granted by the Guangxi Medical University Cancer Hospital Ethical Review Committee (LW2023019).Te patients provided written informed consent for the publication of their anonymized information in this article.Tis retrospective study was carried out in compliance with the STROBE guidelines [11].

EBUS-TBNA Procedure and Pathological Diagnosis.
Patients were examined with a convex probe ultrasound bronchoscope (CP-EBUS; BF-UC260FW, Olympus, Tokyo, Japan) under moderate sedation with midazolam or propofol and local anesthesia with lidocaine.An ultrasound bronchoscope with a frequency of 10 MHz was used for scanning, and an ultrasound device (Eu-ME1 processor, Olympus) was used to generate images to record the sonographic features of lymph nodes.A dedicated 22-gauge needle (Olympus, NA-201XS-4022) was used for lymph node puncture.Each lymph node was punctured 2-5 times.
Te tissue obtained by EBUS-TBNA was fxed in formalin, and the remaining aspirates were smeared on glass slides and fxed with 95% ethanol.Finally, the treated specimens were submitted for examination.Any positive histology or cytology of the puncture specimen was judged to be positive.Te fnal diagnosis of malignant lymph nodes was determined by EBUS-TBNA's malignant cytological and/or histological fndings or surgical and pathological confrmation.Postoperative pathological results of EBUS-TBNA were benign, but the imaging fndings were highly suspected of malignant lesions.Te samples were obtained in other ways and confrmed by pathological examination.If the abovementioned methods still fail to rule out malignant lesions, radiological and clinical follow-up will be carried out for at least 6 months.

EBUS Image Categories.
We evaluated ultrasound features according to the following nine categories (Figure 1) [12]: long-axis (cm), short-axis (>1 cm or <1 cm), long-axis/ short-axis ratio (<1.5 or ≥1.5), echogenicity (heterogeneous or homogeneous), margin (distinct or indistinct), blood fow (rich or lacking), fusion (absent or present), CHS (absent or present), and echo intensity (hypoechoic or isoechoic or hyperechoic).Echo intensity was defned as hypoechoic, isoechoic, and hyperechoic contrasted with the surrounding tissue.Heterogeneous echogenicity was defned as several small areas of varying echogenicity, but do not contain major vascular structures.Distinct margin was defned as more than half of the margin was visible.Fusion was defned as multiple lymph nodes fused into a single lymph node station.CHS was defned as a linear, fat, hyperechoic region in the center of the lymph node.Blood fow was defned as rich and lacking, with lacking suggesting grades 0-1, whereas rich suggesting grades 2-3.
A comparison of the EBUS feature of each lymph node with the fnal diagnosis was conducted to determine the predictive accuracy of malignant lymph nodes.

Statistical Analyses.
Descriptive statistics were reported as frequencies with percentages or interquartile ranges (ranges).Te training and validation sets were randomly grouped in a 7 : 3 ratio.Comparison of training and validation sets was performed by using the Mann-Whitney U test (continuous variables) and the chi-square test (categorical variables).Univariate and multivariate analyses of EBUS features predicting the accuracy of malignant lymph nodes were performed by using logistic regression models.A nomogram for predicting the malignant lymph nodes and the risk stratifcation of the EBUS-TBNA postoperative negative lymph nodes were developed by using a logistic regression model.Nomogram's accuracy in predicting was evaluated by using the receiver operating characteristic (ROC) curve and the area under the curve (AUC).Calibration curves were used for evaluating the goodness of ft of the nomogram.Decision curve analysis (DCA) and clinical impact curve (CIC) were conducted to estimate the net clinical benefts.Statistics were considered signifcant at a P value < 0.05 (two-sided).

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International Journal of Clinical Practice Te statistical analysis was performed with R software (version 4.1.3).Te logistic regression analysis, nomogram construction plots, and nomogram calibration plots were used by the "rms" package.Te DCA and CIC were performed using the "rmda" package.A ROC curve analysis was conducted using the "pROC" package.

Patients and Lymph
Nodes.In total, 686 patients (1235 lesions) underwent EBUS-TBNA.61 patients (153 lesions) were excluded due to the inability to obtain lymph node tissue, loss of follow-up or missing data, and loss of EBUS image or poor EBUS image quality.About 1082 lesions of 625 patients were analyzed.A fowchart illustrating the recruitment of lymph nodes and patients is shown in Figure 2.
Te pathological diagnosis of each lymph node is shown in Table 3. EBUS-TBNA pathology diagnosed 765 lymph nodes as malignant and 317 lymph nodes as benign.Te fnal diagnosis report diagnosed 838 lymph nodes as malignant and 244 lymph nodes as benign.

Developing and Validating a Nomogram to Predict Malignant Lymph
Nodes.1082 lymph nodes were randomly divided into a training set (n � 760) and a validation set (n � 322) in a 7 : 3 ratio.Te ultrasound image features within the training and validation sets did not difer signifcantly, except for the number of passes per node (P � 0.017) (Table 4).
A summary of the results of the univariate and multivariate logistic regression analyses of the ultrasound image features in the training set is shown in Table 5.In the univariate analysis, smoking index (P � 0.01), long-axis (P < 0.001), short-axis (P < 0.001), echogenicity (P < 0.001), blood fow (P � 0.002), fusion (P � 0.002), and CHS (P < 0.001) were associated with a malignant lymph node.In the multivariate analysis, long-axis (P < 0.001), short-axis (P � 0.005), echogenicity (P < 0.001), fusion (P � 0.002), and CHS (P < 0.001) were the independent impact factors of malignancy outcomes.According to these features, 5 EBUS features nomogram was constructed (Figure 3).Prediction of malignancy outcomes could be obtained by summing each point (the total points).
Both the training and validation sets of the 5 EBUS features nomogram were accurate in predicting malignancy outcomes (Figures 4(a  Te nomogram also demonstrated strong predictive capabilities for various cancer cell types, including lung cancer and non-lung cancer lymph nodes.Notably, it achieved a high accuracy in predicting the diagnostic yield, as evidenced by AUC values of 0.750 (sensitivity � 0.809 and specifcity � 0.609) for lung cancer lymph nodes and 0.698 (sensitivity � 0.744 and specifcity � 0.533) for non-lung cancer lymph nodes.Furthermore, the nomogram exhibits favorable performance in predicting the diagnostic rates of diferent pathologic types of lung cancer.Specifcally, the AUC values for adenocarcinoma, squamous carcinoma, small cell carcinoma, and other lung cancers were 0.669 (sensitivity � 0.531 and specifcity � 0.814), 0.729 (sensitivity � 0.867 and specifcity � 0.600), 0.742 (sensitivity � 0.632 and specifcity � 0.760), and 0.682 (sensitivity � 0.767 and specifcity � 0.594), respectively (Supplementary Table 2).4
According to these features, 3 EBUS features nomogram was constructed (Figure 6).A prediction of malignancy outcomes in the EBUS-TBNA postoperative negative lymph nodes can be obtained by summing each point (the total points).
Both the training and validation sets of the 3 EBUS features nomogram were accurate in predicting malignancy outcomes (Figures 7(a 8(c) and 8(d)).73.5% of the EBUS-TBNA postoperative negative lymph nodes were classifed as other benign lymph nodes, and it was also important to recognize malignant outcomes in these lymph nodes.Supplementary Table 3 shows that the nomogram accurately predicted other benign lymph nodes with an AUC of 0.874 (sensitivity � 0.847 and specifcity � 0.783).

Discussion
In this study, we successfully established a systematic scoring model based on two nomograms to distinguish benign/ malignant lymph nodes and EBUS-TBNA postoperative negative lymph nodes.In predicting malignant lymph nodes, our 5 EBUS features nomogram consisted of longaxis, short-axis, echogenicity, fusion, and CHS.Te optimal AUC value for this nomogram was 0.905 which was better than the Canada LN score (AUC � 0.72) and eight EBUS features (AUC � 0.857) [13,14] and also had a good predictive efcacy in predicting various cancer cell types.In predicting malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes, our 3 EBUS features nomogram consisted of long-axis, echogenicity, and CHS.Te optimal AUC value for this nomogram was 0.89, which was of high sensitivity and specifcity.It was the frst nomogram that predicted the malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes only based on EBUS features [10].
Several studies have evaluated the diagnostic performance of each EBUS feature and scoring model based on EBUS features for predicting malignant lymph nodes [8,[13][14][15][16][17][18][19].Among these studies, Fujiwara et al. were the frst to report on EBUS features for predicting malignant lymph nodes [8].487 patients and 1061 lymph nodes were analyzed retrospectively.A distinct margin, round shape, heterogeneous echogenicity, and coagulation necrosis sign were independent predictors of metastasis in multivariate analysis, each with an OR of 3.05, 3.1, 1.96, and 5.64.Morishita et al. reported on multi-EBUS features [14].A total of 597 lymph nodes were evaluated retrospectively from 302 patients.Among a multivariate analysis of metastasis risk, short-axis (>1 cm), absence of CHS, heterogeneous echogenicity, presence of CNS, and blue-dominant images were the most predictive factors, with odds ratios of 1.86, 1.901, 20.4,3.86, and 3.46.In addition, Morishita et al. drew ROC curves based on the results of multivariate analysis, eight EBUS features, and six B-mode features, with AUC values of 0.894, 0.857, and 0.84.Diagnostic parameters of EBUS features were diferent in each study.Our results show that the absence of CHS (OR � 13.11) and heterogeneous echogenicity (OR � 5.46) have a strong ability to predict malignant lymph nodes compared with the remaining EBUS features.Several studies have found similar trends [14,15].In addition, long-axis, short-axis (>1 cm), and absence of fusion were also found to be associated with predicting malignant lymph nodes in our study.Interestingly, there were few studies on long-axis and fusion.Only Wang et al.International Journal of Clinical Practice reported that the long-axis (>1.67 cm) was more accurate at predicting malignant lymph nodes than the short-axis [19].
Our study also showed a similar result.Te long-axis (>1.67 cm) had a higher diagnostic accuracy for predicting malignant lymph nodes than the short-axis (>1 cm) in our 5 EBUS features nomogram (Figure 3).Since there were few studies on the long-axis, the optimal cut-of value of the long-axis was controversial, so we   International Journal of Clinical Practice analyzed the long-axis as a continuous variable.Te absence of fusion was an independent predictive factor of malignant lymph nodes in our study.However, this outcome was contrary to that of Wang et al. who found that the presentation of fusion was an independent predictive factor of malignant lymph nodes [19].Tese outcomes must be interpreted with caution because the presence of fusion could be seen in both benign and malignant diseases [20].
Few previous studies have focused on EBUS-TBNA postoperative negative lymph nodes, and only Evison et al. investigated a risk stratifcation model to categorise EBUS-TBNA postoperative negative lymph nodes based on EBUS, CT, and PET [10].Tis retrospective study included 329 lymph nodes.Lymph node SUV, the SUV ratio, and heterogeneous echogenicity were independently predictive of malignancy in EBUS-TBNA postoperative negative        [13].Scoring ≥3 suggests biopsy.Te AUC of this model was 0.72, which was of high sensitivity and specifcity.Compared with the previous studies [13,19], our two nomograms displayed excellent performance.Both nomograms showed a good predictive ability (AUC � 0.905 and 0.89), a good diagnostic accuracy (the highest accuracy of both was 90%), and a high clinical net beneft (DCA and CIC analysis) in predicting malignant lymph nodes.Te good predictive performance and ease of use made these two nomograms easy to formulate strategies in the real world.Tis study has some limitations.First, the study was retrospective and conducted at a single center, so it may have sufered from a selection bias.In addition, the sample size for predicting malignancy outcomes of EBUS-TBNA postoperative negative lymph nodes was small, which could have afected the credibility of this study.Hence, an external validation with multicenters and larger samples might be the best option.Furthermore, patients from diverse backgrounds participated in this study.Diferent benign and malignant diseases, for example, lymphoma, and granulomatous infammation usually show diferent EBUS patterns [9,20].As a result, their results may difer.

Conclusions
Evaluation of lymph nodes with EBUS sonographic features would predict malignant lymph nodes and malignancy outcomes in the EBUS-TBNA postoperative negative lymph nodes.Our novel scoring system using the 5 EBUS features nomogram (long-axis, short-axis, echogenicity, fusion, and CHS) and 3 EBUS features nomogram (long-axis, echogenicity, and CHS) is useful for predicting malignant lymph nodes.

ROC:
Receiver operating characteristic SCLC: Small cell lung cancer.

2. 1 .
Study Patients.Data for patients who underwent EBUS-TBNA due to unclear diagnosis of mediastinal enlarged lymph nodes in the Endoscopic Diagnosis Center of the Afliated Cancer Hospital of Guangxi Medical University from February 2016 to June 2019 were analyzed retrospectively.Inclusion criteria included the following: (1) age 18 or older, (2) patients who underwent chest enhanced CT before EBUS-TBNA examination to assess the nature of mediastinal and hilar enlarged lymph nodes, (3) complete EBUS-TBNA and obtain lymph node tissue for histological and cytological examination, and (4) complete clinical and imaging data.Exclusion criteria included the following: (1) unable to obtain lymph node tissue by EBUS-TBNA, (2) loss of EBUS image or poor EBUS image quality, and (3) loss of follow-up or missing data.
) and 4(b)).Tis nomogram had an AUC of 0.880 (sensitivity � 0.829 and specifcity � 0.807) in the training set and 0.905 (sensitivity � 0.819, specifcity � 0.857) in the validation set.In addition, the calibration plots of the 5 EBUS features nomogram showed good agreement between predicted and actual malignancy outcomes in training and validation sets (Figures4(c) and 4(d)).Te DCA shows that the 5 EBUS features nomogram had a good predictive efciency in the training set and validation sets (Figures 5(a) and 5(b)).Te high-risk threshold of the training set was approximately 0-0.8 and that of the
) and 7(b)).Tis nomogram had an AUC of 0.890 (sensitivity � 0.882 and specifcity � 0.786) in the training set and 0.834 (sensitivity � 0.930 and specifcity � 0.636) in the validation set.In addition, calibration plots of the 3 EBUS features nomogram showed a good agreement between predicted and actual malignancy outcomes in training and validation sets (Figures7(c) and 7(d)).Te DCA shows that the 3 EBUS features nomogram had a good predictive efciency in the training set and validation sets (Figures 8(a) and 8(b)).Te high-risk threshold of the training set was approximately 0-0.9 and that of the validation set was approximately 0.08-0.75,which was the most benefcial for the prediction of malignancy outcomes in the EBUS-TBNA postoperative negative lymph nodes.Te CICs were established based on the 3 EBUS features nomogram DCA to help us more intuitively comprehend their substantial value (Figures

Figure 4 :
Figure 4: Te performance of 5 EBUS features nomogram in the training set and the validation set.(a) ROC curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the training dataset.(b) ROC curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation dataset.(c) Calibration curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the training dataset.(d) Calibration curve of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation dataset.

Figure 5 :
Figure 5: DCA and CIC of 5 EBUS features nomogram in the training set and the validation set.(a) DCA of 5 EBUS features nomogram for predicting malignant lymph nodes in the training set.(b) DCA of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation set.(c) CIC of 5 EBUS features nomogram for predicting malignant lymph nodes in the training set.(d) CIC of 5 EBUS features nomogram for predicting malignant lymph nodes in the validation set.CIC, clinical impact curve; DCA, decision curve analysis.

Table 1 :
NegativeLymph Nodes.317EBUS-TBNApostoperativenegativelymphnodes were randomly divided into a training set (n � 224) and a validation set (n � 93) in a 7 : 3 ratio.Te ultrasound image features within the training and validation sets did not difer signifcantly, except for the echo intensity (P � 0.033) (Table6).A summary of the results of the univariate and multivariate logistic regression analyses of the ultrasound image features in the training set is shown in Table7.In the Clinical characteristics.

Table 2 :
Ultrasound image features of all lymph nodes.

Table 3 :
Pathological diagnosis of all lymph nodes.

Table 4 :
Ultrasound image features for predicting malignant lymph nodes in the training and validation sets.

Table 5 :
Univariate and multivariate logistic regression analyses of the training set in the whole cohort.
Researchers developed some scoring systems to explore the best cut-ofs by combining several features.Wang et al. developed a scoring system based on nonhilar perfusion, presence of matting, absence of CHS, and round shape, with a diagnostic accuracy range of 24.57-82.68%and

Table 6 :
Ultrasound image features for predicting the malignancy outcomes of lymph nodes deemed negative following EBUS-TBNA in the training and validation sets.

Table 7 :
Univariate and multivariate logistic regression analyses of the training set for the EBUS-TBNA diagnosed benign cohort.